Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations4440
Missing cells3990
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 MiB
Average record size in memory2.0 KiB

Variable types

Text9
Numeric2
Categorical3

Alerts

Cited by is highly overall correlated with YearHigh correlation
Document Type is highly overall correlated with Open AccessHigh correlation
Open Access is highly overall correlated with Document TypeHigh correlation
Year is highly overall correlated with Cited byHigh correlation
Language of Original Document is highly imbalanced (93.8%) Imbalance
Document Type is highly imbalanced (68.0%) Imbalance
Author full names has 97 (2.2%) missing values Missing
Author(s) ID has 97 (2.2%) missing values Missing
Affiliations has 107 (2.4%) missing values Missing
Authors with affiliations has 107 (2.4%) missing values Missing
Author Keywords has 1042 (23.5%) missing values Missing
Open Access has 2528 (56.9%) missing values Missing
Cited by is highly skewed (γ1 = 44.55171865) Skewed
EID has unique values Unique
Cited by has 1620 (36.5%) zeros Zeros

Reproduction

Analysis started2024-11-01 19:27:20.956753
Analysis finished2024-11-01 19:27:25.270592
Duration4.31 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Author full names
Text

Missing 

Distinct4070
Distinct (%)93.7%
Missing97
Missing (%)2.2%
Memory size811.2 KiB
2024-11-01T19:27:25.625923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length928
Median length297
Mean length120.31798
Min length21

Characters and Unicode

Total characters522541
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3856 ?
Unique (%)88.8%

Sample

1st rowPuri, Rishabh (59230247300); Onishi, Junya (7003942439); Rüttgers, Mario (57205467321); Sarma, Rakesh (56448274800); Tsubokura, Makoto (57543345100); Lintermann, Andreas (36573883800)
2nd rowBanerjee, Srutarshi (57212204932); Rodrigues, Miesher (24483782300); Ballester, Manuel (57336856100); Vija, Alexander H. (8521136100); Katsaggelos, Aggelos K. (7102711302)
3rd rowAgraz, Melih (57188574966)
4th rowCen, Jianhuan (58172058200); Zou, Qingsong (8369763900)
5th rowYao, Liaojun (54584768400); Wang, Jiexiong (57776191800); Chuai, Mingyue (58485210500); Lomov, Stepan V. (7005067917); Carvelli, V. (6603539304)
ValueCountFrequency (%)
wang 711
 
1.3%
zhang 589
 
1.0%
li 558
 
1.0%
liu 456
 
0.8%
chen 400
 
0.7%
yang 289
 
0.5%
a 266
 
0.5%
m 231
 
0.4%
xu 207
 
0.4%
wu 198
 
0.3%
Other values (25216) 52870
93.1%
2024-11-01T19:27:26.551817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52413
 
10.0%
0 37703
 
7.2%
5 27599
 
5.3%
a 24348
 
4.7%
7 20822
 
4.0%
n 20003
 
3.8%
2 19252
 
3.7%
i 18273
 
3.5%
( 17910
 
3.4%
) 17910
 
3.4%
Other values (105) 266308
51.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 522541
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
52413
 
10.0%
0 37703
 
7.2%
5 27599
 
5.3%
a 24348
 
4.7%
7 20822
 
4.0%
n 20003
 
3.8%
2 19252
 
3.7%
i 18273
 
3.5%
( 17910
 
3.4%
) 17910
 
3.4%
Other values (105) 266308
51.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 522541
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
52413
 
10.0%
0 37703
 
7.2%
5 27599
 
5.3%
a 24348
 
4.7%
7 20822
 
4.0%
n 20003
 
3.8%
2 19252
 
3.7%
i 18273
 
3.5%
( 17910
 
3.4%
) 17910
 
3.4%
Other values (105) 266308
51.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 522541
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
52413
 
10.0%
0 37703
 
7.2%
5 27599
 
5.3%
a 24348
 
4.7%
7 20822
 
4.0%
n 20003
 
3.8%
2 19252
 
3.7%
i 18273
 
3.5%
( 17910
 
3.4%
) 17910
 
3.4%
Other values (105) 266308
51.0%

Author(s) ID
Text

Missing 

Distinct4006
Distinct (%)92.2%
Missing97
Missing (%)2.2%
Memory size461.5 KiB
2024-11-01T19:27:26.996094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length395
Median length252
Mean length51.061248
Min length10

Characters and Unicode

Total characters221759
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3751 ?
Unique (%)86.4%

Sample

1st row59230247300; 7003942439; 57205467321; 56448274800; 57543345100; 36573883800
2nd row57212204932; 24483782300; 57336856100; 8521136100; 7102711302
3rd row57188574966
4th row58172058200; 8369763900
5th row54584768400; 57776191800; 58485210500; 7005067917; 6603539304
ValueCountFrequency (%)
55665002100 76
 
0.4%
7003655679 36
 
0.2%
8453033200 23
 
0.1%
57370095000 23
 
0.1%
12645288600 21
 
0.1%
57034330100 18
 
0.1%
55870784900 18
 
0.1%
35194560500 17
 
0.1%
57192679570 16
 
0.1%
56145252100 16
 
0.1%
Other values (12511) 17642
98.5%
2024-11-01T19:27:27.868809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37702
17.0%
5 27599
12.4%
7 20822
9.4%
2 19252
8.7%
1 16551
7.5%
6 15978
7.2%
9 14501
 
6.5%
3 14405
 
6.5%
8 14333
 
6.5%
; 13563
 
6.1%
Other values (2) 27053
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 221759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37702
17.0%
5 27599
12.4%
7 20822
9.4%
2 19252
8.7%
1 16551
7.5%
6 15978
7.2%
9 14501
 
6.5%
3 14405
 
6.5%
8 14333
 
6.5%
; 13563
 
6.1%
Other values (2) 27053
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 221759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37702
17.0%
5 27599
12.4%
7 20822
9.4%
2 19252
8.7%
1 16551
7.5%
6 15978
7.2%
9 14501
 
6.5%
3 14405
 
6.5%
8 14333
 
6.5%
; 13563
 
6.1%
Other values (2) 27053
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 221759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37702
17.0%
5 27599
12.4%
7 20822
9.4%
2 19252
8.7%
1 16551
7.5%
6 15978
7.2%
9 14501
 
6.5%
3 14405
 
6.5%
8 14333
 
6.5%
; 13563
 
6.1%
Other values (2) 27053
12.2%

Title
Text

Distinct4394
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size711.7 KiB
2024-11-01T19:27:28.426442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length547
Median length177
Mean length99.953153
Min length15

Characters and Unicode

Total characters443792
Distinct characters237
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4377 ?
Unique (%)98.6%

Sample

1st rowOn the choice of physical constraints in artificial neural networks for predicting flow fields
2nd rowA physics based machine learning model to characterize room temperature semiconductor detectors in 3D
3rd rowEvaluating single multiplicative neuron models in physics-informed neural networks for differential equations
4th rowDeep finite volume method for partial differential equations
5th rowPhysics-informed machine learning for loading history dependent fatigue delamination of composite laminates
ValueCountFrequency (%)
for 2335
 
4.3%
neural 2236
 
4.1%
physics-informed 2111
 
3.9%
of 1905
 
3.5%
networks 1356
 
2.5%
learning 1348
 
2.5%
and 1213
 
2.2%
a 1108
 
2.0%
network 888
 
1.6%
in 886
 
1.6%
Other values (7047) 38681
71.5%
2024-11-01T19:27:29.558187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49468
 
11.1%
e 37136
 
8.4%
i 32703
 
7.4%
n 30554
 
6.9%
o 27896
 
6.3%
r 27220
 
6.1%
a 25714
 
5.8%
t 23072
 
5.2%
s 22078
 
5.0%
l 15215
 
3.4%
Other values (227) 152736
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 443792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
49468
 
11.1%
e 37136
 
8.4%
i 32703
 
7.4%
n 30554
 
6.9%
o 27896
 
6.3%
r 27220
 
6.1%
a 25714
 
5.8%
t 23072
 
5.2%
s 22078
 
5.0%
l 15215
 
3.4%
Other values (227) 152736
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 443792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
49468
 
11.1%
e 37136
 
8.4%
i 32703
 
7.4%
n 30554
 
6.9%
o 27896
 
6.3%
r 27220
 
6.1%
a 25714
 
5.8%
t 23072
 
5.2%
s 22078
 
5.0%
l 15215
 
3.4%
Other values (227) 152736
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 443792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
49468
 
11.1%
e 37136
 
8.4%
i 32703
 
7.4%
n 30554
 
6.9%
o 27896
 
6.3%
r 27220
 
6.1%
a 25714
 
5.8%
t 23072
 
5.2%
s 22078
 
5.0%
l 15215
 
3.4%
Other values (227) 152736
34.4%

Year
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2022.7995
Minimum2016
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.8 KiB
2024-11-01T19:27:29.859998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2020
Q12022
median2023
Q32024
95-th percentile2024
Maximum2025
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2890094
Coefficient of variation (CV)0.0006372403
Kurtosis2.7706132
Mean2022.7995
Median Absolute Deviation (MAD)1
Skewness-1.3803014
Sum8981230
Variance1.6615452
MonotonicityNot monotonic
2024-11-01T19:27:30.104088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2024 1603
36.1%
2023 1348
30.4%
2022 841
18.9%
2021 400
 
9.0%
2020 150
 
3.4%
2019 51
 
1.1%
2017 17
 
0.4%
2018 12
 
0.3%
2025 10
 
0.2%
2016 8
 
0.2%
ValueCountFrequency (%)
2016 8
 
0.2%
2017 17
 
0.4%
2018 12
 
0.3%
2019 51
 
1.1%
2020 150
 
3.4%
2021 400
 
9.0%
2022 841
18.9%
2023 1348
30.4%
2024 1603
36.1%
2025 10
 
0.2%
ValueCountFrequency (%)
2025 10
 
0.2%
2024 1603
36.1%
2023 1348
30.4%
2022 841
18.9%
2021 400
 
9.0%
2020 150
 
3.4%
2019 51
 
1.1%
2018 12
 
0.3%
2017 17
 
0.4%
2016 8
 
0.2%
Distinct1467
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size436.6 KiB
2024-11-01T19:27:30.532136image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length267
Median length131
Mean length43.667793
Min length4

Characters and Unicode

Total characters193885
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique833 ?
Unique (%)18.8%

Sample

1st rowFuture Generation Computer Systems
2nd rowScientific Reports
3rd rowScientific Reports
4th rowJournal of Computational Physics
5th rowComposites Part A: Applied Science and Manufacturing
ValueCountFrequency (%)
and 1624
 
6.5%
of 1444
 
5.8%
journal 789
 
3.2%
in 784
 
3.1%
engineering 773
 
3.1%
conference 623
 
2.5%
international 621
 
2.5%
on 598
 
2.4%
proceedings 559
 
2.2%
ieee 439
 
1.8%
Other values (1608) 16701
66.9%
2024-11-01T19:27:31.747637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20521
 
10.6%
n 17994
 
9.3%
e 17272
 
8.9%
i 14117
 
7.3%
o 12212
 
6.3%
a 11227
 
5.8%
t 9967
 
5.1%
r 9389
 
4.8%
c 8628
 
4.5%
s 8189
 
4.2%
Other values (64) 64369
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 193885
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
20521
 
10.6%
n 17994
 
9.3%
e 17272
 
8.9%
i 14117
 
7.3%
o 12212
 
6.3%
a 11227
 
5.8%
t 9967
 
5.1%
r 9389
 
4.8%
c 8628
 
4.5%
s 8189
 
4.2%
Other values (64) 64369
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 193885
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
20521
 
10.6%
n 17994
 
9.3%
e 17272
 
8.9%
i 14117
 
7.3%
o 12212
 
6.3%
a 11227
 
5.8%
t 9967
 
5.1%
r 9389
 
4.8%
c 8628
 
4.5%
s 8189
 
4.2%
Other values (64) 64369
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 193885
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
20521
 
10.6%
n 17994
 
9.3%
e 17272
 
8.9%
i 14117
 
7.3%
o 12212
 
6.3%
a 11227
 
5.8%
t 9967
 
5.1%
r 9389
 
4.8%
c 8628
 
4.5%
s 8189
 
4.2%
Other values (64) 64369
33.2%

Cited by
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct182
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.777027
Minimum0
Maximum6648
Zeros1620
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size34.8 KiB
2024-11-01T19:27:32.362952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile51.05
Maximum6648
Range6648
Interquartile range (IQR)8

Descriptive statistics

Standard deviation116.71375
Coefficient of variation (CV)7.898324
Kurtosis2411.3149
Mean14.777027
Median Absolute Deviation (MAD)2
Skewness44.551719
Sum65610
Variance13622.099
MonotonicityNot monotonic
2024-11-01T19:27:32.960484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1620
36.5%
1 531
 
12.0%
2 323
 
7.3%
3 233
 
5.2%
4 217
 
4.9%
5 132
 
3.0%
6 129
 
2.9%
7 108
 
2.4%
9 84
 
1.9%
10 83
 
1.9%
Other values (172) 980
22.1%
ValueCountFrequency (%)
0 1620
36.5%
1 531
 
12.0%
2 323
 
7.3%
3 233
 
5.2%
4 217
 
4.9%
5 132
 
3.0%
6 129
 
2.9%
7 108
 
2.4%
8 77
 
1.7%
9 84
 
1.9%
ValueCountFrequency (%)
6648 1
< 0.1%
2632 1
< 0.1%
1082 1
< 0.1%
942 1
< 0.1%
633 1
< 0.1%
563 1
< 0.1%
543 1
< 0.1%
542 1
< 0.1%
525 1
< 0.1%
511 1
< 0.1%

Affiliations
Text

Missing 

Distinct4077
Distinct (%)94.1%
Missing107
Missing (%)2.4%
Memory size1.3 MiB
2024-11-01T19:27:33.647848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length2210
Median length570
Mean length231.86407
Min length3

Characters and Unicode

Total characters1004667
Distinct characters132
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3894 ?
Unique (%)89.9%

Sample

1st rowJülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Engler-Bunte Institute, Combustion Technology, Karlsruhe Institute for Technology, Engler-Bunte Ring 7, Karlsruhe, 76131, Germany
2nd rowNorthwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United States
3rd rowDepartment of Statistics, Giresun University, Giresun, 28200, Turkey
4th rowSchool of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China; School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, 510006, China
5th rowDepartment of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Department of Materials Engineering, KU Leuven, Belgium; Department A.B.C., Politecnico di Milano, Italy
ValueCountFrequency (%)
of 11585
 
9.1%
university 6786
 
5.3%
and 4928
 
3.9%
engineering 4144
 
3.3%
united 3302
 
2.6%
department 3280
 
2.6%
china 3274
 
2.6%
states 2945
 
2.3%
technology 1979
 
1.6%
science 1821
 
1.4%
Other values (9532) 83068
65.4%
2024-11-01T19:27:34.940697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
122744
 
12.2%
e 80139
 
8.0%
n 78018
 
7.8%
i 71068
 
7.1%
a 62612
 
6.2%
t 56537
 
5.6%
o 50026
 
5.0%
r 42808
 
4.3%
, 41458
 
4.1%
s 30375
 
3.0%
Other values (122) 368882
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1004667
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
122744
 
12.2%
e 80139
 
8.0%
n 78018
 
7.8%
i 71068
 
7.1%
a 62612
 
6.2%
t 56537
 
5.6%
o 50026
 
5.0%
r 42808
 
4.3%
, 41458
 
4.1%
s 30375
 
3.0%
Other values (122) 368882
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1004667
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
122744
 
12.2%
e 80139
 
8.0%
n 78018
 
7.8%
i 71068
 
7.1%
a 62612
 
6.2%
t 56537
 
5.6%
o 50026
 
5.0%
r 42808
 
4.3%
, 41458
 
4.1%
s 30375
 
3.0%
Other values (122) 368882
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1004667
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
122744
 
12.2%
e 80139
 
8.0%
n 78018
 
7.8%
i 71068
 
7.1%
a 62612
 
6.2%
t 56537
 
5.6%
o 50026
 
5.0%
r 42808
 
4.3%
, 41458
 
4.1%
s 30375
 
3.0%
Other values (122) 368882
36.7%
Distinct4249
Distinct (%)98.1%
Missing107
Missing (%)2.4%
Memory size2.8 MiB
2024-11-01T19:27:35.652458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length3824
Median length1071
Mean length534.994
Min length15

Characters and Unicode

Total characters2318129
Distinct characters143
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4178 ?
Unique (%)96.4%

Sample

1st rowPuri R., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany, Engler-Bunte Institute, Combustion Technology, Karlsruhe Institute for Technology, Engler-Bunte Ring 7, Karlsruhe, 76131, Germany; Onishi J., RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Rüttgers M., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; Sarma R., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; Tsubokura M., RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Lintermann A., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany
2nd rowBanerjee S., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Rodrigues M., Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United States; Ballester M., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Vija A.H., Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United States; Katsaggelos A.K., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States
3rd rowAgraz M., Department of Statistics, Giresun University, Giresun, 28200, Turkey
4th rowCen J., School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China; Zou Q., School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, 510006, China
5th rowYao L., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Wang J., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Chuai M., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Lomov S.V., Department of Materials Engineering, KU Leuven, Belgium; Carvelli V., Department A.B.C., Politecnico di Milano, Italy
ValueCountFrequency (%)
of 24547
 
8.1%
university 14393
 
4.7%
and 10607
 
3.5%
engineering 8980
 
3.0%
china 7841
 
2.6%
united 6420
 
2.1%
department 6313
 
2.1%
states 5738
 
1.9%
technology 4349
 
1.4%
school 4028
 
1.3%
Other values (16177) 210414
69.3%
2024-11-01T19:27:36.662127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
299198
 
12.9%
n 174253
 
7.5%
e 173922
 
7.5%
i 158060
 
6.8%
a 143667
 
6.2%
t 118583
 
5.1%
o 112525
 
4.9%
, 107261
 
4.6%
r 94980
 
4.1%
s 66359
 
2.9%
Other values (133) 869321
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2318129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
299198
 
12.9%
n 174253
 
7.5%
e 173922
 
7.5%
i 158060
 
6.8%
a 143667
 
6.2%
t 118583
 
5.1%
o 112525
 
4.9%
, 107261
 
4.6%
r 94980
 
4.1%
s 66359
 
2.9%
Other values (133) 869321
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2318129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
299198
 
12.9%
n 174253
 
7.5%
e 173922
 
7.5%
i 158060
 
6.8%
a 143667
 
6.2%
t 118583
 
5.1%
o 112525
 
4.9%
, 107261
 
4.6%
r 94980
 
4.1%
s 66359
 
2.9%
Other values (133) 869321
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2318129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
299198
 
12.9%
n 174253
 
7.5%
e 173922
 
7.5%
i 158060
 
6.8%
a 143667
 
6.2%
t 118583
 
5.1%
o 112525
 
4.9%
, 107261
 
4.6%
r 94980
 
4.1%
s 66359
 
2.9%
Other values (133) 869321
37.5%

Author Keywords
Text

Missing 

Distinct3387
Distinct (%)99.7%
Missing1042
Missing (%)23.5%
Memory size603.1 KiB
2024-11-01T19:27:37.136587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length695
Median length208
Mean length111.94968
Min length18

Characters and Unicode

Total characters380405
Distinct characters99
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3377 ?
Unique (%)99.4%

Sample

1st rowFluid dynamics; Partial differential equations; Physics-informed neural networks; Simplified NavierStokes equations; Unsteady flow
2nd rowFinite volume method; High-dimensional PDEs; Neural network; Second order differential operator
3rd rowComposite laminates; Delamination; Fatigue; Fiber bridging; Physics-informed neural networks (PINNs)
4th rowKolmogorov n-width; Multitask learning; Neural operators; Physics-informed neural networks (PINNs)
5th rowComposite laminated plates; Inverse problem; Physical-constrained neural networks; Transfer learning
ValueCountFrequency (%)
neural 2476
 
6.2%
learning 2116
 
5.3%
physics-informed 1806
 
4.5%
networks 1364
 
3.4%
network 1176
 
3.0%
machine 1134
 
2.8%
deep 854
 
2.1%
model 424
 
1.1%
equation 416
 
1.0%
physics 346
 
0.9%
Other values (5375) 27744
69.6%
2024-11-01T19:27:38.021176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36454
 
9.6%
e 35219
 
9.3%
i 30282
 
8.0%
n 28910
 
7.6%
r 24182
 
6.4%
a 24004
 
6.3%
o 20770
 
5.5%
t 20558
 
5.4%
s 18002
 
4.7%
l 15721
 
4.1%
Other values (89) 126303
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 380405
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
36454
 
9.6%
e 35219
 
9.3%
i 30282
 
8.0%
n 28910
 
7.6%
r 24182
 
6.4%
a 24004
 
6.3%
o 20770
 
5.5%
t 20558
 
5.4%
s 18002
 
4.7%
l 15721
 
4.1%
Other values (89) 126303
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 380405
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
36454
 
9.6%
e 35219
 
9.3%
i 30282
 
8.0%
n 28910
 
7.6%
r 24182
 
6.4%
a 24004
 
6.3%
o 20770
 
5.5%
t 20558
 
5.4%
s 18002
 
4.7%
l 15721
 
4.1%
Other values (89) 126303
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 380405
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
36454
 
9.6%
e 35219
 
9.3%
i 30282
 
8.0%
n 28910
 
7.6%
r 24182
 
6.4%
a 24004
 
6.3%
o 20770
 
5.5%
t 20558
 
5.4%
s 18002
 
4.7%
l 15721
 
4.1%
Other values (89) 126303
33.2%
Distinct286
Distinct (%)6.5%
Missing7
Missing (%)0.2%
Memory size371.8 KiB
2024-11-01T19:27:38.487385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length84
Median length72
Mean length28.815926
Min length4

Characters and Unicode

Total characters127741
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)2.8%

Sample

1st rowElsevier B.V.
2nd rowNature Research
3rd rowNature Research
4th rowAcademic Press Inc.
5th rowElsevier Ltd
ValueCountFrequency (%)
and 1296
 
7.4%
of 1246
 
7.1%
elsevier 1161
 
6.6%
inc 986
 
5.6%
institute 959
 
5.5%
engineers 782
 
4.5%
ltd 780
 
4.5%
electronics 572
 
3.3%
electrical 571
 
3.3%
b.v 538
 
3.1%
Other values (407) 8589
49.1%
2024-11-01T19:27:39.385559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13048
 
10.2%
e 12339
 
9.7%
i 10470
 
8.2%
n 9868
 
7.7%
s 7982
 
6.2%
t 7523
 
5.9%
c 7505
 
5.9%
r 6573
 
5.1%
a 5389
 
4.2%
l 4921
 
3.9%
Other values (56) 42123
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13048
 
10.2%
e 12339
 
9.7%
i 10470
 
8.2%
n 9868
 
7.7%
s 7982
 
6.2%
t 7523
 
5.9%
c 7505
 
5.9%
r 6573
 
5.1%
a 5389
 
4.2%
l 4921
 
3.9%
Other values (56) 42123
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13048
 
10.2%
e 12339
 
9.7%
i 10470
 
8.2%
n 9868
 
7.7%
s 7982
 
6.2%
t 7523
 
5.9%
c 7505
 
5.9%
r 6573
 
5.1%
a 5389
 
4.2%
l 4921
 
3.9%
Other values (56) 42123
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13048
 
10.2%
e 12339
 
9.7%
i 10470
 
8.2%
n 9868
 
7.7%
s 7982
 
6.2%
t 7523
 
5.9%
c 7505
 
5.9%
r 6573
 
5.1%
a 5389
 
4.2%
l 4921
 
3.9%
Other values (56) 42123
33.0%

Language of Original Document
Categorical

Imbalance 

Distinct8
Distinct (%)0.2%
Missing4
Missing (%)0.1%
Memory size277.6 KiB
English
4333 
Chinese
 
85
Japanese
 
4
Korean
 
4
German
 
4
Other values (3)
 
6

Length

Max length9
Median length7
Mean length7.0009017
Min length6

Characters and Unicode

Total characters31056
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English 4333
97.6%
Chinese 85
 
1.9%
Japanese 4
 
0.1%
Korean 4
 
0.1%
German 4
 
0.1%
Icelandic 3
 
0.1%
Russian 2
 
< 0.1%
undefined 1
 
< 0.1%
(Missing) 4
 
0.1%

Length

2024-11-01T19:27:39.746319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-01T19:27:40.052341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
english 4333
97.7%
chinese 85
 
1.9%
japanese 4
 
0.1%
korean 4
 
0.1%
german 4
 
0.1%
icelandic 3
 
0.1%
russian 2
 
< 0.1%
undefined 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 4437
14.3%
s 4426
14.3%
i 4424
14.2%
h 4418
14.2%
l 4336
14.0%
E 4333
14.0%
g 4333
14.0%
e 191
 
0.6%
C 85
 
0.3%
a 21
 
0.1%
Other values (13) 52
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 4437
14.3%
s 4426
14.3%
i 4424
14.2%
h 4418
14.2%
l 4336
14.0%
E 4333
14.0%
g 4333
14.0%
e 191
 
0.6%
C 85
 
0.3%
a 21
 
0.1%
Other values (13) 52
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 4437
14.3%
s 4426
14.3%
i 4424
14.2%
h 4418
14.2%
l 4336
14.0%
E 4333
14.0%
g 4333
14.0%
e 191
 
0.6%
C 85
 
0.3%
a 21
 
0.1%
Other values (13) 52
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 4437
14.3%
s 4426
14.3%
i 4424
14.2%
h 4418
14.2%
l 4336
14.0%
E 4333
14.0%
g 4333
14.0%
e 191
 
0.6%
C 85
 
0.3%
a 21
 
0.1%
Other values (13) 52
 
0.2%

Document Type
Categorical

High correlation  Imbalance 

Distinct16
Distinct (%)0.4%
Missing1
Missing (%)< 0.1%
Memory size288.8 KiB
Article
3020 
Conference paper
1140 
Conference review
 
92
Review
 
87
Book chapter
 
62
Other values (11)
 
38

Length

Max length23
Median length7
Mean length9.5733273
Min length4

Characters and Unicode

Total characters42496
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowArticle
2nd rowArticle
3rd rowArticle
4th rowArticle
5th rowArticle

Common Values

ValueCountFrequency (%)
Article 3020
68.0%
Conference paper 1140
 
25.7%
Conference review 92
 
2.1%
Review 87
 
2.0%
Book chapter 62
 
1.4%
Erratum 13
 
0.3%
Note 6
 
0.1%
Editorial 5
 
0.1%
Book 4
 
0.1%
Data paper 3
 
0.1%
Other values (6) 7
 
0.2%

Length

2024-11-01T19:27:40.385113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
article 3020
52.6%
conference 1232
21.5%
paper 1143
 
19.9%
review 179
 
3.1%
book 66
 
1.1%
chapter 62
 
1.1%
erratum 13
 
0.2%
note 6
 
0.1%
editorial 5
 
0.1%
data 3
 
0.1%
Other values (12) 14
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 8293
19.5%
r 5588
13.1%
c 4315
10.2%
i 3211
 
7.6%
t 3116
 
7.3%
l 3027
 
7.1%
A 3021
 
7.1%
n 2467
 
5.8%
p 2348
 
5.5%
o 1377
 
3.2%
Other values (26) 5733
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8293
19.5%
r 5588
13.1%
c 4315
10.2%
i 3211
 
7.6%
t 3116
 
7.3%
l 3027
 
7.1%
A 3021
 
7.1%
n 2467
 
5.8%
p 2348
 
5.5%
o 1377
 
3.2%
Other values (26) 5733
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8293
19.5%
r 5588
13.1%
c 4315
10.2%
i 3211
 
7.6%
t 3116
 
7.3%
l 3027
 
7.1%
A 3021
 
7.1%
n 2467
 
5.8%
p 2348
 
5.5%
o 1377
 
3.2%
Other values (26) 5733
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8293
19.5%
r 5588
13.1%
c 4315
10.2%
i 3211
 
7.6%
t 3116
 
7.3%
l 3027
 
7.1%
A 3021
 
7.1%
n 2467
 
5.8%
p 2348
 
5.5%
o 1377
 
3.2%
Other values (26) 5733
13.5%

Open Access
Categorical

High correlation  Missing 

Distinct10
Distinct (%)0.5%
Missing2528
Missing (%)56.9%
Memory size314.0 KiB
All Open Access; Green Open Access
567 
All Open Access; Gold Open Access
554 
All Open Access; Hybrid Gold Open Access
343 
All Open Access; Bronze Open Access
238 
All Open Access; Gold Open Access; Green Open Access
124 
Other values (5)
86 

Length

Max length59
Median length54
Mean length37.055962
Min length4

Characters and Unicode

Total characters70851
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowAll Open Access; Hybrid Gold Open Access
2nd rowAll Open Access; Gold Open Access
3rd rowAll Open Access; Gold Open Access
4th rowAll Open Access; Green Open Access
5th rowAll Open Access; Green Open Access

Common Values

ValueCountFrequency (%)
All Open Access; Green Open Access 567
 
12.8%
All Open Access; Gold Open Access 554
 
12.5%
All Open Access; Hybrid Gold Open Access 343
 
7.7%
All Open Access; Bronze Open Access 238
 
5.4%
All Open Access; Gold Open Access; Green Open Access 124
 
2.8%
All Open Access; Green Open Access; Hybrid Gold Open Access 69
 
1.6%
All Open Access; Bronze Open Access; Green Open Access 13
 
0.3%
Final 2
 
< 0.1%
final 1
 
< 0.1%
Book 1
 
< 0.1%
(Missing) 2528
56.9%

Length

2024-11-01T19:27:40.715582image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-01T19:27:41.270455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
open 4022
32.2%
access 4022
32.2%
all 1908
15.3%
gold 1090
 
8.7%
green 773
 
6.2%
hybrid 412
 
3.3%
bronze 251
 
2.0%
final 3
 
< 0.1%
book 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
10571
14.9%
e 9841
13.9%
c 8044
11.4%
s 8044
11.4%
A 5930
8.4%
n 5049
7.1%
l 4909
6.9%
O 4022
 
5.7%
p 4022
 
5.7%
; 2114
 
3.0%
Other values (14) 8305
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10571
14.9%
e 9841
13.9%
c 8044
11.4%
s 8044
11.4%
A 5930
8.4%
n 5049
7.1%
l 4909
6.9%
O 4022
 
5.7%
p 4022
 
5.7%
; 2114
 
3.0%
Other values (14) 8305
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10571
14.9%
e 9841
13.9%
c 8044
11.4%
s 8044
11.4%
A 5930
8.4%
n 5049
7.1%
l 4909
6.9%
O 4022
 
5.7%
p 4022
 
5.7%
; 2114
 
3.0%
Other values (14) 8305
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10571
14.9%
e 9841
13.9%
c 8044
11.4%
s 8044
11.4%
A 5930
8.4%
n 5049
7.1%
l 4909
6.9%
O 4022
 
5.7%
p 4022
 
5.7%
; 2114
 
3.0%
Other values (14) 8305
11.7%

EID
Text

Unique 

Distinct4440
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size325.3 KiB
2024-11-01T19:27:41.681741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters79920
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4440 ?
Unique (%)100.0%

Sample

1st row2-s2.0-85199357735
2nd row2-s2.0-85189357733
3rd row2-s2.0-85201391255
4th row2-s2.0-85200113536
5th row2-s2.0-85204075898
ValueCountFrequency (%)
2-s2.0-85199357735 1
 
< 0.1%
2-s2.0-85203655512 1
 
< 0.1%
2-s2.0-85200113536 1
 
< 0.1%
2-s2.0-85204075898 1
 
< 0.1%
2-s2.0-85204053479 1
 
< 0.1%
2-s2.0-85201877813 1
 
< 0.1%
2-s2.0-85204690331 1
 
< 0.1%
2-s2.0-85200217550 1
 
< 0.1%
2-s2.0-85204772188 1
 
< 0.1%
2-s2.0-85197687438 1
 
< 0.1%
Other values (4430) 4430
99.8%
2024-11-01T19:27:42.379306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 12764
16.0%
- 8880
11.1%
0 8261
10.3%
8 8200
10.3%
5 7620
9.5%
1 7066
8.8%
s 4440
 
5.6%
. 4440
 
5.6%
9 3902
 
4.9%
7 3675
 
4.6%
Other values (3) 10672
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 12764
16.0%
- 8880
11.1%
0 8261
10.3%
8 8200
10.3%
5 7620
9.5%
1 7066
8.8%
s 4440
 
5.6%
. 4440
 
5.6%
9 3902
 
4.9%
7 3675
 
4.6%
Other values (3) 10672
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 12764
16.0%
- 8880
11.1%
0 8261
10.3%
8 8200
10.3%
5 7620
9.5%
1 7066
8.8%
s 4440
 
5.6%
. 4440
 
5.6%
9 3902
 
4.9%
7 3675
 
4.6%
Other values (3) 10672
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 12764
16.0%
- 8880
11.1%
0 8261
10.3%
8 8200
10.3%
5 7620
9.5%
1 7066
8.8%
s 4440
 
5.6%
. 4440
 
5.6%
9 3902
 
4.9%
7 3675
 
4.6%
Other values (3) 10672
13.4%

Interactions

2024-11-01T19:27:23.558398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-01T19:27:23.139856image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-01T19:27:23.775085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-01T19:27:23.338322image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-01T19:27:42.631955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Cited byDocument TypeLanguage of Original DocumentOpen AccessYear
Cited by1.0000.0220.0000.000-0.585
Document Type0.0221.0000.2330.5030.140
Language of Original Document0.0000.2331.0000.0260.179
Open Access0.0000.5030.0261.0000.129
Year-0.5850.1400.1790.1291.000

Missing values

2024-11-01T19:27:24.130289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-01T19:27:24.629474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-01T19:27:25.038036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Author full namesAuthor(s) IDTitleYearSource titleCited byAffiliationsAuthors with affiliationsAuthor KeywordsPublisherLanguage of Original DocumentDocument TypeOpen AccessEID
0Puri, Rishabh (59230247300); Onishi, Junya (7003942439); Rüttgers, Mario (57205467321); Sarma, Rakesh (56448274800); Tsubokura, Makoto (57543345100); Lintermann, Andreas (36573883800)59230247300; 7003942439; 57205467321; 56448274800; 57543345100; 36573883800On the choice of physical constraints in artificial neural networks for predicting flow fields2024Future Generation Computer Systems0Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Engler-Bunte Institute, Combustion Technology, Karlsruhe Institute for Technology, Engler-Bunte Ring 7, Karlsruhe, 76131, GermanyPuri R., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany, Engler-Bunte Institute, Combustion Technology, Karlsruhe Institute for Technology, Engler-Bunte Ring 7, Karlsruhe, 76131, Germany; Onishi J., RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Rüttgers M., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; Sarma R., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, Germany; Tsubokura M., RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Hyogo, Kobe, 650-0047, Japan; Lintermann A., Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich, 52425, GermanyFluid dynamics; Partial differential equations; Physics-informed neural networks; Simplified NavierStokes equations; Unsteady flowElsevier B.V.EnglishArticleAll Open Access; Hybrid Gold Open Access2-s2.0-85199357735
1Banerjee, Srutarshi (57212204932); Rodrigues, Miesher (24483782300); Ballester, Manuel (57336856100); Vija, Alexander H. (8521136100); Katsaggelos, Aggelos K. (7102711302)57212204932; 24483782300; 57336856100; 8521136100; 7102711302A physics based machine learning model to characterize room temperature semiconductor detectors in 3D2024Scientific Reports0Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United StatesBanerjee S., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Rodrigues M., Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United States; Ballester M., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United States; Vija A.H., Siemens Medical Solutions USA, Inc., Hoffman Estates, 60192, IL, United States; Katsaggelos A.K., Northwestern University, 2145 Sheridan Road, Evanston, 60208, IL, United StatesNaNNature ResearchEnglishArticleAll Open Access; Gold Open Access2-s2.0-85189357733
2Agraz, Melih (57188574966)57188574966Evaluating single multiplicative neuron models in physics-informed neural networks for differential equations2024Scientific Reports0Department of Statistics, Giresun University, Giresun, 28200, TurkeyAgraz M., Department of Statistics, Giresun University, Giresun, 28200, TurkeyNaNNature ResearchEnglishArticleAll Open Access; Gold Open Access2-s2.0-85201391255
3Cen, Jianhuan (58172058200); Zou, Qingsong (8369763900)58172058200; 8369763900Deep finite volume method for partial differential equations2024Journal of Computational Physics0School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China; School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, 510006, ChinaCen J., School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China; Zou Q., School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, 510006, ChinaFinite volume method; High-dimensional PDEs; Neural network; Second order differential operatorAcademic Press Inc.EnglishArticleAll Open Access; Green Open Access2-s2.0-85200113536
4Yao, Liaojun (54584768400); Wang, Jiexiong (57776191800); Chuai, Mingyue (58485210500); Lomov, Stepan V. (7005067917); Carvelli, V. (6603539304)54584768400; 57776191800; 58485210500; 7005067917; 6603539304Physics-informed machine learning for loading history dependent fatigue delamination of composite laminates2024Composites Part A: Applied Science and Manufacturing0Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Department of Materials Engineering, KU Leuven, Belgium; Department A.B.C., Politecnico di Milano, ItalyYao L., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Wang J., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Chuai M., Department of Astronautics Science and Mechanics, Harbin Institute of Technology, Harbin, China; Lomov S.V., Department of Materials Engineering, KU Leuven, Belgium; Carvelli V., Department A.B.C., Politecnico di Milano, ItalyComposite laminates; Delamination; Fatigue; Fiber bridging; Physics-informed neural networks (PINNs)Elsevier LtdEnglishArticleNaN2-s2.0-85204075898
5Penwarden, Michael (57225220864); Owhadi, Houman (6602245780); Kirby, Robert M. (7201552592)57225220864; 6602245780; 7201552592Kolmogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics2024Neural Networks0Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, 84112, UT, United States; Kahlert School of Computing, University of Utah, Salt Lake City, 84112, UT, United States; Department of Computing and Mathematical Sciences, Caltech, Pasadena, 91125, CA, United StatesPenwarden M., Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, 84112, UT, United States, Kahlert School of Computing, University of Utah, Salt Lake City, 84112, UT, United States; Owhadi H., Department of Computing and Mathematical Sciences, Caltech, Pasadena, 91125, CA, United States; Kirby R.M., Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, 84112, UT, United States, Kahlert School of Computing, University of Utah, Salt Lake City, 84112, UT, United StatesKolmogorov n-width; Multitask learning; Neural operators; Physics-informed neural networks (PINNs)Elsevier LtdEnglishArticleAll Open Access; Green Open Access2-s2.0-85204053479
6Li, Yang (59012879300); Wan, Detao (56999850900); Wang, Zhe (59032214000); Hu, Dean (36602497000)59012879300; 56999850900; 59032214000; 36602497000Physics-constrained deep learning approach for solving inverse problems in composite laminated plates2024Composite Structures0Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment, Ministry of Education, Hunan University, Changsha, 410082, ChinaLi Y., Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment, Ministry of Education, Hunan University, Changsha, 410082, China; Wan D., Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment, Ministry of Education, Hunan University, Changsha, 410082, China; Wang Z., Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment, Ministry of Education, Hunan University, Changsha, 410082, China; Hu D., Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment, Ministry of Education, Hunan University, Changsha, 410082, ChinaComposite laminated plates; Inverse problem; Physical-constrained neural networks; Transfer learningElsevier LtdEnglishArticleNaN2-s2.0-85201877813
7Sen, Ahmet (59110359400); Ghajar-Rahimi, Elnaz (57218339760); Aguirre, Miquel (55977418200); Navarro, Laurent (57211805601); Goergen, Craig J. (22134531900); Avril, Stephane (6602427519)59110359400; 57218339760; 55977418200; 57211805601; 22134531900; 6602427519Physics-Informed Graph Neural Networks to solve 1-D equations of blood flow2024Computer Methods and Programs in Biomedicine0Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059, Sainbiose, F-42023, France; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, 47907, IN, United States; CIMNE, Gran Capità, 08034, Spain; LaCàN, Universitat Politècnica de Catalunya, Jordi Girona 1, Barcelona, E-08034, SpainSen A., Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059, Sainbiose, F-42023, France; Ghajar-Rahimi E., Weldon School of Biomedical Engineering, Purdue University, West Lafayette, 47907, IN, United States; Aguirre M., CIMNE, Gran Capità, 08034, Spain, LaCàN, Universitat Politècnica de Catalunya, Jordi Girona 1, Barcelona, E-08034, Spain; Navarro L., Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059, Sainbiose, F-42023, France; Goergen C.J., Weldon School of Biomedical Engineering, Purdue University, West Lafayette, 47907, IN, United States; Avril S., Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059, Sainbiose, F-42023, FranceBlood flow modeling; Graph neural network; Machine learning; Physics-Informed Neural Networks; Pulse wave propagationElsevier Ireland LtdEnglishArticleAll Open Access; Hybrid Gold Open Access2-s2.0-85204690331
8Hedayatrasa, Saeid (55845267000); Fink, Olga (54961523800); Van Paepegem, Wim (9640465200); Kersemans, Mathias (54958029400)55845267000; 54961523800; 9640465200; 54958029400k-space physics-informed neural network (k-PINN) for compressed spectral mapping and efficient inversion of vibrations in thin composite laminates2025Mechanical Systems and Signal Processing0Mechanics of Materials and Structures (UGent-MMS), Department of Materials, Textiles and Chemical Engineering (MaTCh), Ghent University, Technologiepark-Zwijnaarde 46, Zwijnaarde, 9052, Belgium; Flanders Make-MotionS, Lommel, 3920, Belgium; Intelligent Maintenance and Operating Systems (IMOS) lab, EPFL, SwitzerlandHedayatrasa S., Mechanics of Materials and Structures (UGent-MMS), Department of Materials, Textiles and Chemical Engineering (MaTCh), Ghent University, Technologiepark-Zwijnaarde 46, Zwijnaarde, 9052, Belgium, Flanders Make-MotionS, Lommel, 3920, Belgium; Fink O., Intelligent Maintenance and Operating Systems (IMOS) lab, EPFL, Switzerland; Van Paepegem W., Mechanics of Materials and Structures (UGent-MMS), Department of Materials, Textiles and Chemical Engineering (MaTCh), Ghent University, Technologiepark-Zwijnaarde 46, Zwijnaarde, 9052, Belgium; Kersemans M., Mechanics of Materials and Structures (UGent-MMS), Department of Materials, Textiles and Chemical Engineering (MaTCh), Ghent University, Technologiepark-Zwijnaarde 46, Zwijnaarde, 9052, BelgiumComposite; Elastic Coefficients; Inversion; K-space; Physics-informed Neural Networks; Spectral bias; VibrationAcademic PressEnglishArticleNaN2-s2.0-85203655512
9Duong, Tien Trung (57205516680); Jung, Kwang Hyo (7402479754); Lee, Gang Nam (57191165522); Suh, Sung Bu (35791359900)57205516680; 7402479754; 57191165522; 35791359900Physics-informed neural network for the reconstruction of velocity and pressure of wave-in-deck loading from particle image velocimetry data2024Applied Ocean Research0Department of Naval Architecture and Ocean Engineering, Pusan National University, 2, Busandaehak-ro, Busan, 46241, South Korea; Department of Ocean System Engineering, Jeju National University, 102 Jejudaehak-ro, Jeju, Jeju-si, 63243, South Korea; Department of Naval Architecture and Ocean Engineering, Dong-Eui University, 176, Eomgwang-ro, Busan, 47340, South KoreaDuong T.T., Department of Naval Architecture and Ocean Engineering, Pusan National University, 2, Busandaehak-ro, Busan, 46241, South Korea; Jung K.H., Department of Naval Architecture and Ocean Engineering, Pusan National University, 2, Busandaehak-ro, Busan, 46241, South Korea; Lee G.N., Department of Ocean System Engineering, Jeju National University, 102 Jejudaehak-ro, Jeju, Jeju-si, 63243, South Korea; Suh S.B., Department of Naval Architecture and Ocean Engineering, Dong-Eui University, 176, Eomgwang-ro, Busan, 47340, South KoreaEuler equation; Particle image velocimetry; Physics-informed neural network; Pressure fields; Velocity profile; Wave-in-deck loadElsevier LtdEnglishArticleNaN2-s2.0-85202944549
Author full namesAuthor(s) IDTitleYearSource titleCited byAffiliationsAuthors with affiliationsAuthor KeywordsPublisherLanguage of Original DocumentDocument TypeOpen AccessEID
4430Butler, Philip (58180559800)58180559800Humanism and the Conceptualization of Value and Well-Being2019The oxford Handbook of Humanism0Theology and Black Posthuman Artificial Intelligence Systems, Iliff School of Theology, United StatesButler P., Theology and Black Posthuman Artificial Intelligence Systems, Iliff School of Theology, United StatesAfrican American humanism; decolonial studies; epistemology; humanism; philosophy; spirituality; transcendence; well-beingOxford University PressEnglishBook chapterNaN2-s2.0-85133092793
4431Zhang, Dongkun (57200302259); Lu, Lu (57189226888); Guo, Ling (44461317100); Karniadakis, George Em (55665002100)57200302259; 57189226888; 44461317100; 55665002100Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems2019Journal of Computational Physics271Division of Applied Mathematics, Brown University, Providence, RI, United States; Department of Mathematics, Shanghai Normal University, Shanghai, ChinaZhang D., Division of Applied Mathematics, Brown University, Providence, RI, United States; Lu L., Division of Applied Mathematics, Brown University, Providence, RI, United States; Guo L., Department of Mathematics, Shanghai Normal University, Shanghai, China; Karniadakis G.E., Division of Applied Mathematics, Brown University, Providence, RI, United StatesArbitrary polynomial chaos; Dropout; Physics-informed neural networks; Stochastic differential equations; Uncertainty quantificationAcademic Press Inc.EnglishArticleAll Open Access; Bronze Open Access2-s2.0-85070222554
4432Simmons, Cody R. (57215008255); Arment, Joshua R. (57214998078); Powell, Kody M. (51461952600); Hedengren, John D. (9277159100)57215008255; 57214998078; 51461952600; 9277159100Proactive energy optimization in residential buildings withweather and market forecasts2019Processes14Department of Chemical Engineering, Brigham Young University, Provo, 84602, UT, United States; Department of Chemical Engineering, University of Utah, Salt Lake City, 84112, UT, United StatesSimmons C.R., Department of Chemical Engineering, Brigham Young University, Provo, 84602, UT, United States; Arment J.R., Department of Chemical Engineering, Brigham Young University, Provo, 84602, UT, United States; Powell K.M., Department of Chemical Engineering, University of Utah, Salt Lake City, 84112, UT, United States; Hedengren J.D., Department of Chemical Engineering, Brigham Young University, Provo, 84602, UT, United StatesDynamic optimization; Energy storage; Forecast; HEMS; Home energy optimization; Model predictive control; Moving horizon estimation; Solar generation; Thermal modelingMDPI AGEnglishArticleAll Open Access; Gold Open Access; Green Open Access2-s2.0-85079639165
4433Werhahn, Maximilian (57219619923); Xie, You (57204697503); Chu, Mengyu (57196000654); Thuerey, Nils (26024848800)57219619923; 57204697503; 57196000654; 26024848800A multi-pass GaN for fluid flow super-resolution2019Proceedings of the ACM on Computer Graphics and Interactive Techniques42Technical University of Munich, GermanyWerhahn M., Technical University of Munich, Germany; Xie Y., Technical University of Munich, Germany; Chu M., Technical University of Munich, Germany; Thuerey N., Technical University of Munich, GermanyComputer animation; Fluid simulation; Generative models; Physics-based deep learningAssociation for Computing MachineryEnglishArticleAll Open Access; Green Open Access2-s2.0-85092777667
4434Pinn, Anthony B. (37096219900); Driscoll, Christopher M. (57091215500)37096219900; 57091215500Introduction: K.dotting the american cultural landscape with black meaning2019Kendrick Lamar and the Making of Black Meaning0Rice University, United States; Lehigh University, United StatesPinn A.B., Rice University, United States; Driscoll C.M., Lehigh University, United StatesNaNTaylor and FrancisEnglishBook chapterNaN2-s2.0-85084877159
4435Guan, Haowen (57188932152); Li, Qingzhong (56413173800); Yan, Zhongmin (55286550700); Wei, Wei (57020492500)57188932152; 56413173800; 55286550700; 57020492500SLOF: Identify density-based local outliers in big data2016Proceedings - 2015 12th Web Information System and Application Conference, WISA 201511School of Computer Science and Technology, Shandong University, Jinan, China; Shandong Hoteam Software Co., Ltd., Jinan, ChinaGuan H., School of Computer Science and Technology, Shandong University, Jinan, China; Li Q., School of Computer Science and Technology, Shandong University, Jinan, China; Yan Z., School of Computer Science and Technology, Shandong University, Jinan, China; Wei W., Shandong Hoteam Software Co., Ltd., Jinan, ChinaData mining; Density-based outlier detection; Feature bagging; LOF; PINN; SLOFInstitute of Electrical and Electronics Engineers Inc.EnglishConference paperNaN2-s2.0-84964306898
4436Chang, Chih-Wei (57142655400); Dinh, Nam (57208696068)57142655400; 57208696068A study of physics-informed deep learning for system fluid dynamics closures2016Transactions of the American Nuclear Society6North Carolina State University, Raleigh, 27695-7909, NC, United StatesChang C.-W., North Carolina State University, Raleigh, 27695-7909, NC, United States; Dinh N., North Carolina State University, Raleigh, 27695-7909, NC, United StatesNaNAmerican Nuclear SocietyEnglishConference paperNaN2-s2.0-85033222005
4437Liu, Lu (56176397700); Wang, Dan (57842016600); Peng, Zhouhua (35230939100)56176397700; 57842016600; 35230939100Path following of marine surface vehicles with dynamical uncertainty and time-varying ocean disturbances2016Neurocomputing95School of Marine Engineering, Dalian Maritime University, Dalian, 116026, ChinaLiu L., School of Marine Engineering, Dalian Maritime University, Dalian, 116026, China; Wang D., School of Marine Engineering, Dalian Maritime University, Dalian, 116026, China; Peng Z., School of Marine Engineering, Dalian Maritime University, Dalian, 116026, ChinaIterative updating law; Line-of-sight; Marine surface vehicles; Neural network; Path following; PredictorElsevier B.V.EnglishArticleNaN2-s2.0-84959326260
4438Már, Hjalti (57193699116)57193699116Pekkir pú pinn rétt? Um FOSL2016Laeknabladid0FOSL, IcelandMár H., FOSL, IcelandNaNLaeknafelag IslandsIcelandicNoteNaN2-s2.0-84978208543
4439Porkelsson, Eyjólfur (36715292300)36715292300"Ég trúi pví, sannleiki, ao sigurinn pinn ao síoustu vegina jafni"2016Laeknabladid0Skáni, IcelandPorkelsson E., Skáni, IcelandNaNLaeknafelag IslandsIcelandicNoteNaN2-s2.0-84978245375